Strategy Intern – Anomaly Detection Platform

Aioi R&D Lab – Oxford is an AI R&D company based in Oxford, on a mission to harness AI to understand, predict, and manage risk, helping build a safer, more resilient society.

We sit at the intersection of academia and industry, working with Oxford's professors, researchers, and graduates alongside commercial spinouts and partner companies, to turn frontier research into AI that actually ships rather than just gets published.

Our work spans applied AI for insurance and adjacent industries, including supply chains, nature, autonomous driving, and the emerging challenges nobody's solved yet, alongside deep research of our own into agentic AI, privacy-preserving technologies, trustworthy AI, complex systems modelling, and quantum computing.

We build AI products and solutions for insurers, businesses, and public-sector organisations worldwide, and run innovative research projects that push these technologies further, helping people make better decisions in an uncertain world.

The Strategy Intern – Anomaly Detection Platform will produce a rigorous, evidence-based go-to-market strategy for Aioi R&D Lab's cross-modal anomaly detection technology.

The Lab has developed anomaly detection technology that surfaces inconsistencies across multiple data modalities — tables, free text, images and more — with particular strength in spotting patterns that link different data types together.

Early applications in fraud detection have been highly successful in motor insurance, home insurance and motor warranty claims.

The Lab now wishes to test whether the same reusable core technology can power multiple products across different domains, with applications adapted case by case while the underlying engine stays constant.

The purpose of the role is to ensure:

• Candidate use cases for cross-modal anomaly detection are identified and assessed beyond insurance fraud, across industries and functions.
• Each use case is mapped against the technology's reusable platform layers, testing which layers generalise across domains and which would need to be rebuilt case by case.
• Historical and contemporary platform business models are analysed to identify the factors behind their success or failure.
• The technology is translated into workflow-specific propositions for each audience, rather than pitched as a single abstract “anomaly detection” capability.
• Findings are synthesised into a single strategic recommendation, including a proposed go-to-market strategy, blue-ocean opportunities and clearly flagged open questions.